Development and Validation of An Interpretable Machine Learning-Based Prediction Model of Postpartum Hemorrhage in Placenta Previa Following Cesarean Section: A Multicenter Study.

Journal: Reproductive sciences (Thousand Oaks, Calif.)
Published Date: Aug 12, 2025

Abstract

The objective of this study is to predict the occurrence of postpartum hemorrhage in women with placenta previa based on machine learning. This retrospective study enrolled 845 singleton pregnant patients with placenta previa from two hospitals. They were allocated into a training cohort (n = 403), a testing cohort (n = 174), and the external validation cohort (n = 268). Univariate and multivariate regression analyses were employed to select clinical variables (p < 0.05), which were subsequently utilized to develop 11 machine learning prediction models. The area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were used to evaluate the performance of the models. Besides, SHapley Additive exPlanations (SHAP) was used to interpret the role and effectiveness of variables in the predictive model. Three machine learning models with the best predictive performance were combined into a Prediction Ensemble Classifier through voting. The Gradient Boosting Machine demonstrated the best predictive performance. In the validation cohort, AUC of the Gradient Boosting Machine model is 0.810(95% CI 0.754-0.865), ACC was 0.765(95% CI 0.716-0.813), SEN was 0.613(95% CI 0.513-0.723), while these values of the Prediction Ensemble Classifier were 0.813(0.756-0.871), 0.806(0.757-0.854), and 0.480(0.375-0.597), respectively. The importance of SHAP variables in the model, ranked from high to low, is as follows: d-dimer, ultrasound diagnosis of placenta accreta spectrum, neutrophils, prothrombin time, and platelets. The Gradient Boosting Machine model demonstrated excellent performance in predicting postpartum hemorrhage in cases of placenta previa. Furthermore, SHAP analysis enabled interpretation of the variables in the model.

Authors

  • Mianmian Li
    Department of Laboratory Medicine, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.
  • Xinhui Su
    Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
  • Wenxin Liao
    Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei Province, China.
  • Li Huang
    National Research Center for Resettlement (NRCR), Hohai University, 1 Xikang Road, Nanjing 210098, China. lily8214@hhu.edu.cn.
  • Yihong Yang
    Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, 200120, Shanghai, People's Republic of China.
  • Xizi Wu
    Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
  • Yao Fan
    Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
  • Jing Liu
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Xin Yang
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Zhen Zeng
    Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China.
  • Wencheng Ding
    Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
  • Wanjiang Zeng
    Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
  • Xiaoyan Xu
    School of Control Science and Engineering, Shandong University, Jinan 250061, China.

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